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Answer: Deploy the model using provisioned throughput as it comes with performance guarantees
The question focuses on ensuring a pay-per-token Foundation Model API endpoint can handle high request volumes in production. Option D (Deploy the model using provisioned throughput as it comes with performance guarantees) is optimal because provisioned throughput specifically addresses scalability and performance needs by guaranteeing capacity, which aligns with production requirements for high-volume traffic. Option A (Switch to using External Models instead) is less suitable as it involves changing the model deployment approach rather than optimizing the current setup. Option B (Throttle the incoming batch of requests manually) is suboptimal because manual throttling does not scale and may not effectively manage high volumes. Option C (Change to a model with fewer parameters) is irrelevant, as it addresses hardware constraints rather than request volume scalability. The community discussion supports D, with 67% of answers favoring it and the highest upvoted comment endorsing it.
Author: LeetQuiz Editorial Team
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A Generative AI Engineer has built an LLM application using a pay-per-token Foundation Model API. As they prepare for production deployment, how can they ensure the model endpoint can handle high volumes of incoming requests?
A
Switch to using External Models instead
B
Throttle the incoming batch of requests manually to avoid rate limiting issues
C
Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
D
Deploy the model using provisioned throughput as it comes with performance guarantees
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